Abstract:
Abstract: This research work presents the composition of the pseudostem of selected varieties of banana and detailed characteristics of the fibre in its intact form in the stem. Banana is one of the important fruit crops cultivated in tropical parts of the world. Non-food products like yarn, fabrics and quality papers are manufactured from the pseudostem. But its utilization in the food industry is limited to candy, pickles and ready to serve juices. The Pseudostem is rich in fibre content in addition to essential nutrients possessing numerous health benefits. The Pseudostem from different banana cultivars has been evaluated for fibre yield through physico-chemical and morphological analysis including macro and micro characters such as the shape, size, colour and fibre characteristics. The moisture, fat, protein, ash and fibre contents of the pseudostem have been determined. Morphological analysis was done through image processing using LabView software. The results obtained from both the methods indicate that the quantity and quality of the pseudostem fibre is distinct for each variety.
KEYWORDS: Image processing, Food application, macro and micro characters, Size and shape.
INTRODUCTION
Banana (Musa paradisiaca) is one of the most important, enormously grown and the oldest cultivated fruit crops grown almost everywhere in India. All over the world, close to a hundred common names exist which denote the fruit from the genus Musa , with over 1000 cultivars and landraces emanating from more than fifty Musa specie (Heslop-Harrison & Schwarzacher 2007; Arvanitoyannis & Mavromatis 2009). Reports suggest that the contemporary name banana may have been given by the natives of coastal population of Western region of Africa, Sierra Leone or Guinea. It was later held as a consensus in the contemporary world as a term used to denote the fruit’s peel.
Apart from water, the pseudostem of banana contains several polymers such as cellulose, hemicellulose, pectin and lignin that constitute fibres with good mechanical properties. It has a lesser quantum of extractives, protein, starch, and inorganics. The best fibre of banana has been widely recognized for its good qualities over synthetic fibres and is used for making apparels, garments, and home furnishing (Ercisli et al., 2012, Font et al., 2014, Manickavasagan et al., 2014, Mahajana et al., 2015, Muhammad 2015, Aghilinategh et al., 2016).
Presently, banana pseudostem is a hazardous waste in India, whilst it has been used in many nations to produce significant bio-based commodities like fibre to produce yarn, apparel, fabric, fertilizer, fish foodd, bio-chemicals, handicrafts, pickles, candy, etc.,
Some fruits and vegetable have been characterized based on their sizes, shapes, textures, colours etc. The classification of date fruits using shape and size features fused with texture descriptors showed highest accuracy (Muhammad 2015). Quantitative differentiation in cucumber cultivars at varying maturity stages based on fruit textures and shapes were analysed (Shimomura et al., 2016). The size and shape features such as diameter, perimeter, length, width, thickness, mass and volume were used to distinguish walnut cultivars from each other using principal component analysis (Ercisli et al., 2011). The Grading of mangoes based on the geometry and shape by extracting the projected area, perimeter and roundness features using global thresholding color binarization combined with median filter was developed (Momin, 2017).
Machine vision for orchard fruit load estimation requires imaging using artificial lighting and the algorithm consists of filtering using RGB and YCbCr with edge detection by Hessian filter (Payne et al., 2014). Stem and leaf morphology character of the diploid level was different from the triploid level of the root and rhizome of banana with reference to the size and number of stomata distribution, number of subsidiary cells, hypodermal layer and the structure of the vascular bundle (Sumardi & Wulandari, 2010).
Quality is an important factor for the marketing of banana pseudostem, especially when intended for fresh consumption. The Physical and chemical attributes are reigned by several parameters, like conditions of the climate, fertilization, the times of cultivar, planting and harvest. However, assessing the quality of pseudostem and whether these are within the standards required by consumers, is essential. The characterization of the different banana cultivars is also an useful information for commercial developmental and programs of breeding that would employ cultivars that exhibit resistance to diseases along with a good quality and quantity.
An image processing algorithm based on region-wise global thresholding color binarization, combined with median filter and morphological analysis was developed to classify mango into one of the three mass grades (Momin et al., 2017). Grading of persimmon fruits into different maturity stages was conducted based on external color for mean values of R, G, B channels and two classifiers namely LDA and QDA- Linear and Quadratic Discriminant Analysis (Mohammadi et al., 2015). A color image analysis procedure to estimate the maturity stage of fresh tomatoes based on aggregated percentage surface area below certain hue angles to indicate tomato maturity index was developed (Choi et al., 1995). Nectarine varieties were identified by combining the histogram values corresponding to different skin color planes and validation was done based on a reference dataset (Font et al., 2014).
In this context, the objective of this work is to identify the morphological characteristics of the pseudostem from banana cultivars through image processing and assess the physiochemical characteristics for its fibre yield.
MATERIALS AND METHODS
I. Sample collection:
Based on some known qualities, better strength, and availability during all seasons, samples of banana pseudostem varieties which are triploid cultivars of Musa acuminate (A) and Musa balbisiana (B) namely Poovan (AAB), Neypoovan (AAB), Rasthali (AAB), Karpuravalli (ABB) and Monthan (ABB) were collected from the fields of Tamil Nadu Banana Growers Association, Namakkal and verified for the variety at the Department of Horticulture, Government of Tamilnadu, Karur, India.
i) Sample preparation for physicochemical Analysis: The banana pseudostem (BPS) samples were cut into small pieces of dimension 2 x 2 cm. The initial moisture content of the banana cultivars was determined by oven drying a small aliquot of the samples. The remaining samples were dried in a tray dryer for 8-16 hours at 60°C. These were further dried in a hot air oven for 4 hours, till a constant weight was achieved, at 60°C. They were ground to a fine powder and sieved in a mesh of ASTM 300-325 and stored in air-tight containers until further analyses.
ii) Sample preparation for morphological Analysis: Another batch from the same freshly collected sample set were subjected to morphological analysis through the image processing method.
II Proximate Analysis:
The proximate composition of the sample was determined using the standard methods of analysis of the Association of Official Analytical Chemists (AOAC, 1995).
1. Moisture: The moisture content was determined using an automated I R moisture balance following the method 934.06 put forth by AOAC.
2. Fat: The total fat content refers to the sum of triglycerides, phospholipids, wax ester, sterols and a minor amount of non-fatty materials. The fat content is most commonly present in a lesser percentage in banana and its by-products. Their presence can be estimated using the AOAC 922.06 through acid hydrolysis method.
3. Protein: The crude protein content was determined by multiplying the Nitrogen content of the sample by a factor of 6.25 according to the AOAC 981.10 method.
4. Ash: Determination of the total ash value was done using the method 923.03 of the AOAC.
5. Fibre: Fibre is one of the most important components of the banana pseudo stem. The fibre content is responsible for the various health benefits that are present, which was estimated using the method 978.10 of the AOAC.
3. 4. 5. III Image processing techniques:
Macroscopic and Microscopic Analysis: The steps involved in Macroscopic Image Processing for Size and Shape is represented in Figure 1a. It shows a schematic representation of the application proposed in this paper. The basic image-processing algorithm has seven steps.
1) The first step consists of capturing of the image.
2) The second step consists of extracting the color plane.
3) The third step is to image mask the extracted color plane at the Region of Interest (ROI) center of the image by using a fixed radius and up to threshold value of step 4.
4) The value is selected by a trial and error procedure.
5) Step 5 consists of particle filtration.
6) Step 6 consists of calibration of the equipment with the standard one to avoid zero error.
7) Step 7 consists of the image analysis.
Colour Image processing:
The selection of a proper image acquisition technique is the preliminary step in developing any machine vision system. Images are used for acquiring information, as this technology aims to duplicate the role of human vision by electronically perceiving and understanding an image. Machine vision system implements the theoretical and algorithmic computations by which useful information about an object or a scene can be automatically extracted and analyzed from an acquired image.
In the banana pseudostem, colour determination becomes important for quality analysis. The Computer vision system is used for grading of agricultural products based on external characteristics such as size and colour. Colour is normally measured using an Ultrascan visible spectrophotometer which gives the output in L*, a*, b* colour space. The Image processing system provides an integrated measurement of colour with size analysis through RGB and HSL colour space. The internal characteristics namely, the thickness and intensity of the fibre determines the yield of fibre.
The individual banana cultivars were transported on a black, non-reflective plastic lid where individual RGB and HSL space color images of each individual pseudostem were obtained. In this work, it was assumed that the individual cultivars on the plastic lid are destined for the fresh market without any skin defects. The image acquisition system is operated with constant diffuse illumination and is configured to obtain non-interpolated RGB color images through CIELAB (8 bits per color plane) in a region of interest (ROI) of 200 x 200 pixels where the relative size of the pseudostem in the image always has a diameter greater than 200 pixels. The color space coordinate values, L*a*b*, were proposed by Mendoza et al. (2006) as the best color space for quantification of color in food materials. The complete transformation from nonlinear RGB values to L*a*b* values were conducted. The average values of L*, a*, and b* were computed for all pixels in images at every time. To increase the uniformity between different images, the L*, a*, and b* values were normalized by computing the ratio of the current values to the corresponding primary values (Niamnuy et al., 2008). L*, a*, and b* are Cartesian coordinates that can be used to calculate the polar coordinates L*H*C. H* (hue angle) which are used to distinguish color in food and chroma (C) which shows the degree of color saturation and is proportional to the intensity of the color.
Microscopic Image Processing
Microscopic Imaging techniques have been the “eyes of science” since the time of ancient history. The microscopic images were obtained by focussing intact fibre across cross sectional area of the stem. The goal of segmentation is to separate the object(s) of interest from the background and other objects which are not of interest (Da Fontoura Costa & Cesar Jr. 2010). The image acquisition system used in this study was Zeiss Primovert Microscope at a magnification of 200X (Anatomical structure of monocot stem focussing thickness of pseudostem fibre with Magnification: 200X) pixel and a Scale Factor of 59.64 nm/pixel. The camera was calibrated by customizing the white balance using a grey card before proceeding with each batch for imaging. A dark box was used to create an imaging chamber in order to avoid backscattering effects from other light sources. All the acquired images were stored in the computer and used for further analysis.
Statistical analysis
The Statistical Package for Social Scientists (SPSS, version 16.0) was used for the analysis of the data obtained. Descriptive statistics as well as students’ t – distribution test, analysis of variance (ANOVA) and Duncan Multiple Range Test were used to interpret the results obtained with the level of significance set at p ≤ 0.05.
RESULTS AND DISCUSSION
I. Proximate analysis of banana pseudostem Cultivars:
The moisture content of the samples exceeded 93% – Monthan 95.60%, Poovan 94.65%, Neypoovan 93.20%, Rasthali 93.85% and Karpuravalli 97.40 % respectively. The moisture content of dried banana pseudostem flour was also found to be different for the varieties due to inherent variation in the cultivars. As shown in table I, Monthan was found to have the highest moisture content (6.32%) and Neypoovan the lowest (2.96%). The moisture content of Neypoovan, Rasthali and Karpooravalli were not significantly different from each other (p>0.05), while their values were significantly lower than that of the Monthan (p<0.05).
Lipid in the Monthan banana stems (3.5%) was lower than other cultivars such as Poovan (5.5%,) Neypoovan (5.36%), Rasthali (5.28%), and Karpooravalli (5%) as presented in Table I. The lipid of the Musa cultivars were no significantly different from one another, except Monthan (p<0.05). The protein content of the five cultivars were also comparable, with Poovan being the highest (4.5%) followed by Monthan (4.2%), Neypoovan and Karpooravalli (4.1%) and Rasthali (3.9%) having the lowest value. All banana samples demonstrated relatively high ash contents, between (17.8% and 21.5%) as well as high crude fibre content, between (18.4 % and 38.4 %). The highest was for Monthan (38.4%), followed by Rasthali (35.5%), Poovan (25.2%), and Karpooravalli (24.6%) with lowest for Neypoovan (18.4%). The ash contents of all samples were significantly different from each other (p<0.05), Neypoovan having the highest value followed by Rasthali, with the Poovan having the lowest.
A higher content of fibre in Monthan indicates that this type of pseudostem can be consumed as a fibre supplement. Dietary fibres are known to aid digestion, absorb water and make stools larger and softer so as to prevent constipation and are known to prevent colon cancer as they prevent waste or toxins from staying in the intestine for too long (Eromosele,1993, Ayoola & Adeyeye, 2009, Egbebi & Bademosi, 2012). The fibre contents of Monthan and Rasthali were not significantly different but were significantly different from that of Poovan, Neypoovan and Karpooravalli (p<0.05).
II. Size of pseudostem by image processing
The camera was fixed at a certain height and at the center of captured area to capture the digital images of the cultivars. The Photos were saved on a computer as color image files and a standard procedure was followed to determine the size and shape features of the musa cultivars.
The shape and the size are important features as they have high levels of discrimination between the types of the musa cultivars. The growers usually consider shape and size features to recognize age. The morphological properties of musa cultivars are the key parameters required for the design of sizing and grading machinery, storage structures and process control.
The acquired images were processed to obtain the diameter and area of the stem by incorporating the height of the cultivars, which were measured using a digital caliper. The shape of cultivars was estimated using the approximation models, as shown in
Fig. 1a, Fig. 2 and Table II.
The diameter of the pseudostems were measured in centimetres along the height of stem at three different locations – upper, middle and lower as indicated in Fig 2. An average of the diameters measured at the three different points was considered as the stem diameter. As shown in Table II, the Monthan cultivar was found to have the largest diameter followed by Neypoovan, Karpuravalli, Poovan and Rasthali. The quantum of the fibre was directly proportional to the diameter of the stem with the same length among the five banana cultivars.
III. Colour image processing
Colour analysis of the five cultivars showed (Table III) that the Lightness (L* ) value was same for almost all varieties. Chlorophyll, carotenoids, and anthocyanin concentrations are important indices for determining the color of food particles. The Lowest a* for Monthan of (1.498) and highest for Neypoovan of (1.045) were observed. The value of yellowness b* was the highest for Poovan of (11.79) and lowest (4.516) for Monthan. Fig. 3 shows the colour analysis and luminance profiles of banana pseudostems from five different cultivars. This study envisages the creation and identification of the histogram feature vector by combining the histogram values corresponding to different colour planes (Fig. 3).
The R, G and B intensities of the cultivars are shown in Figure. 3. In all the varieties, the R component was greater than the G and B components except for Monthan and Rasthali. This is in accordance with the findings of Manickavasagan et al., (2014), who demonstrated that the red color band contributed more than other bands in the grading of banana cultivars. In the Monthan variety, there was no difference in the B component among the three RGB components. Also, there were no differences in R and B components in the Rasthali variety. But in Poovan and Neypoovan, the R, G and B components were significantly different for each class. The contribution of different colour bands was based on application. For example, Manickavasagan et al., (2014) measured the colour properties of the three date varieties (Fard, Khalas & Nagal) from the UAE to develop the classification protocols. It was reported that the R component was suitable for discrimination of Fard and Khalas from Nagal, while the G component was good for the classification of Fard and Khalas. Similarly, the B component would be suitable for differentiating Poovan, Neypoovan and Karpooravalli.
The histogram pictorial representation was accomplished using image analysis. The R component of Poovan, Neypoovan and Karpooravalli was more than that of other cultivars. It showed a wide range of 5 to 255 for different classes (Figure. 3). The G component of the tested cultivars ranged between 5 and 55. In all regions, there were no differences between all the varieties except Karpooravalli. Similarly, the B component of dates varied from 100 to 225. In general, the B value decreased with the level of hardness of musa cultivars. It can be seen from the figure that it decreased gradually from Poovan, Neypoovan, Rasthali, Karpooravalli to Monthan.
IV. Microscopic Analysis
Image processing is used as a best tool for analysis of shape characteristics of agriculture and food products (Ercisli et al., 2012, Font et al., 2014, Manickavasagan et al., 2014, Mahajana et al., 2015, Muhammad 2015, Aghilinategh et al., 2016).
As depicted in Figure 4, the thickness of fibres in terms of their width varied widely among the cultivars. In Poovan, the fibre width ranged from 1968 to 2766 nm; in Neypoovan from 2504 to 2624 nm; in Rasthali 837 to 1442 nm; in Karpooravalli from 536 to 1848 nm and in Monthan from 1371 nm to 2088 nm.
The data from colour photometry exhibited similar profiles in the fibre thickness/width ranging from 2800 to 800 nm (Figure 5). The ranges were as follows: Poovan from 2000 to 2600 nm; Neypoovan from 1200 to 2500; Rasthali from 837 to 1442 nm; Karpooravalli from 1200 to 1848 nm; Monthan from 1371 to 2088 nm. Maximum thickness of the fibre was observed in Poovan and the minimum in Rasthali.
V. Intensity of fibre and Regression Analysis
Banana fibre is largely ligno-cellulosic, obtained from the pseudo-stem of banana plant and is one of the best natural fibres with relatively good mechanical properties. It has good specific strength properties comparable to those of conventional material, like glass fibre but with a lower density. Also they have high strength, light weight, smaller elongation, fire resistance, strong moisture absorption and biodegradability. Detailed results of the physicochemical analysis and extractive values are presented in Figure 6. Monthan, Poovan and Rasthali showed considerable fibre strength characteristics. Figures 6(a) to 6(e) show the impact fracture surfaces of cultivars with respect to fibre intensity for Poovan, Neypoovan, Rasthali, Karpooravalli and Monthan respectively. More fibre crowding is seen in Poovan, Rasthali and Monthan. Fibre pullout is easier in this case. Clean fibre pullout in Karpooravalli, Poovan and Rasthali explains the decreased fibre/matrix interaction compared with Neypoovan. The extent of fibre pullout is lower for Neypoovan due to less elastic nature of fibre.
CONCLUSION
The present study revealed that image analysis is a suitable tool to understand the morphological and fibre characteristics of triploid cultivars of Musa acuminata (A) and Musa balbisiana (B) namely Poovan, Neypoovan and Rasthali (AAB) along with Karpuravalli and Monthan (ABB). Comprehensive studies showed the presence of some important diagnostic characters like fibre, protein, ash and fat content. Crude fibre content obtained through proximate analysis was found to have a positive relationship with the size and intensity of the fibre measured through macroscopic and microscopic image analysis. Image analysis using LabVIEW software for morphological study was found to be in good agreement with the standard method. The size and colour analysis showed distinctive characters among the five banana cultivars.
Thus, image processing can be used to measure the external and internal properties of the intact fibre in the banana pseudo-stem and provides an objective method to grade them for quality. Furthermore, the physicochemical properties and proximate composition implies their prospects for obtaining value added products through incorporation in food products.
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Tables
Table I: Proximate Analysis of banana stem flour
Variety MOISTURE% FAT% Protein% Ash% Fibre%
Poovan 4.67b±0.05 5.50b±0.02 4.5a±0.02 17.8b±0.02 25.2a±0.01
Neypoovan 2.96d±0.06 5.36c±0.02 4.1a±0.02 31.8a±0.03 18.4b±0.02
Rasthali 3.50b±0.02 5.28b±0.06 3.9a±0.02 21.5c±0.02 35.5a±0.02
Karpooravalli 3.70c±0.08 5.00a±0.02 4.1a±0.12 19.36b±0.03 24.6c±0.02
Monthan 6.32a±0.02 3.50c±0.02 4.2a±0.03 18.4b±0.04 38.4a±0.32
All data were mean ± standard deviation of triplicate determinations. Means within a column followed by different superscripts are significantly different (P < 0.05)
(Duncan’s test)
Table II Validation of Image Analysis method on standard based on measurement
Variety Actual Diameter(cm) Predicted Diameter by imaging (cm)
Poovan 4.5a±0.1 4.8a±0.1 4.85b±0.2 4.9a±0.1
NeyPoovan 5.45c±0.2 5.43c±0.2 5.45c±0.1 5.50c±0.1
Rasthali 4.10b±0.1 4.17b±0.1 4.23b±0.2 4.18a±0.1
Karpuravalli 5.65a±0.1 5.40a±0.1 5.23a±0.3 5.44b±0.2
Monthan 6.10a±0.2 5.57a±0.3 5.67b±0.3 5.58c±0.2
All data were mean ± standard deviation of triplicate determinations (P < 0.05)
Means within the same column with the same subscripts were not significantly different (Duncan’s test)
Table III Spectrolino spectrophotometer Colour Measurement
Variety L* a* b* ∆E =L2+a2+b2
Poovan 61.396b±0.1 -1.047b±0.1 11.79a±0.2 62.52c±0.5
NeyPoovan 59.456c±0.2 -1.045a±0.2 8.56d±0.5 60.07a±0.3
Rasthali 58.895c±0.5 -1.234c±0.2 6.957c±0.2 59.31c±0.2
Karpuravalli 58.754b±0.8 -1.456c±0.4 4.623a±0.2 58.95b±0.5
Monthan 57.698b±0.6 -1.498d±0.1 4.516a±0.3 57.88b±0.3
Values are expressed as Mean ± S.D, means in rows with different letters (a-c) are significantly different (p<0.05), based on (Duncan’s test).
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